Multicomponent intervention to reduce daily sedentary time: a randomised controlled trial
Bibliographic record
Abstract
OBJECTIVES: To test the efficacy of a multicomponent technology intervention for reducing daily sedentary time and improving cardiometabolic disease risk among sedentary, overweight university employees. DESIGN: Blinded, randomised controlled trial. SETTING: A large south-eastern university in the USA. PARTICIPANTS: 49 middle-aged, primarily female, sedentary and overweight adults working in sedentary jobs enrolled in the study. A total of 40 participants completed the study. INTERVENTIONS: Participants were randomised to either: (1) an intervention group (N=23; 47.6+9.9 years; 94.1% female; 33.2+4.5 kg/m(2)); (2) or wait-list control group (N=17; 42.6+8.9 years; 86.9% female; 31.7+4.9 kg/m(2)). The intervention group received a theory-based, internet-delivered programme, a portable pedal machine at work and a pedometer for 12 weeks. The wait-list control group maintained their behaviours for 12 weeks. OUTCOME MEASURES: Primary (sedentary and physical activity behaviour measured objectively through StepWatch) and secondary (heart rate, blood pressure, height, weight, waist circumference, per cent body fat, cardiorespiratory fitness, fasting lipids) outcomes were measured at baseline and postintervention (12 weeks). Exploratory outcomes including intervention compliance and process evaluation measures were also assessed postintervention. RESULTS: Compared to controls, the intervention group reduced daily sedentary time (mean change (95%CI): -58.7 min/day (-118.4 to 0.99; p<0.01)) after adjusting for baseline values and monitor wear time. Intervention participants logged on to the website 71.3% of all intervention days, used the pedal machine 37.7% of all working intervention days and pedalled an average of 31.1 min/day. CONCLUSIONS: These findings suggest that the intervention was engaging and resulted in reductions in daily sedentary time among full-time sedentary employees. These findings hold public health significance due to the growing number of sedentary jobs and the potential of these technologies in large-scale worksite programmes. TRIAL REGISTRATION: ClinicalTrials.gov #NCT01371084.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".